"Greg Heath" <firstname.lastname@example.org> wrote in message <email@example.com>... > PLEASE, PLEASE DO NOT TOP POST!!! > > "Carlos Aragon" wrote in message <firstname.lastname@example.org>... > > Greg, thanks in advance. You're helping a lot! > > > > You said: > > > > (..) > > > > The best is to use a modication of NEWRB that allows the input of an initial > > > hidden layer. Then > > > > > > 1. After training with set1, use those weights as initial weights for training with set2 + set1. > > or, if you are lucky > > > > 2. After training with set1, use those weights as initial weights for training with set2 and a "characteristic subset" of set1. The drawback is how to define that characteristic. > > > > > > The reason this works is that each hidden node basis function has local region of influence and a 1-to-1 correspondence with a previous worst classified training vector. > > > > (...) > > > > I'm facing problems to perform this action on matlab. > > That statement is absolutely useless. I thought you wanted my help. > > > Is there any automated way there i can record set1 and then use it to train a set2? > > I have no idea what the second part of that statement means. > > >How could i do it? Actualy, i want my feedforwardnet to recognize 14 sets of diferent motor loads. > > Then simultaneously train on samples or characteristic exemplars from all 14.
> If all of the data is not available at once, do it in stages.
I have all the training and test data, but i dont know how could i do to train 14 training vectors and then validate it with just 1 set to check if the neural net is generalizing well.
> What do you not understand about that? > > Hope that is clear. > > Greg
Tying to be clear about wat i'm doing. here is the code:
ia=linear_train_1(1:5001,4); w=linear_train_1(1:5001,5); tq=linear_train_1(1:5001,2); T1=[198:0.000799840032:202]; % Voltage is between 198V and 202V iateste1=ia_lin_1(1:5001,4); wteste1=ia_lin_1(1:5001,5); P=[T1;ia';w']; % This is the training vector that in this case, trains just 1 set of data. T=[tq']; I want my neural net to recognize 14 samples of [T1;ia;w']. T1 is fix but 'ia'' and 'w'' varies according to the load equation i'm changing on my motor model. The question is How could i train it to recognize those 14 samples? If i make 'Ia'' and 'w'' a matrix of 14 different currents and speed, this neural net do not allow me to test a simple vector like is below.